Pandas Tutorial

Creating Objects

Viewing Data

Selection

Manipulating Data

Grouping Data

Merging, Joining and Concatenating

Working with Date and Time

Working With Text Data

Working with CSV and Excel files

Operations

Visualization

Applications and Projects

Add CSS to the Jupyter Notebook using Pandas

Styling a pandas DataFrame in a Jupyter Notebook can make data visualization more engaging and readable. The styling is achieved using CSS, and pandas provides a simple interface to apply styles to DataFrames.

Here's a tutorial on how to add CSS to a pandas DataFrame in a Jupyter Notebook:

1. Setup:

Start by setting up your Jupyter Notebook environment and create a sample DataFrame:

import pandas as pd
import numpy as np

np.random.seed(24)
df = pd.DataFrame({'A': np.linspace(1, 10, 10)})
df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))], axis=1)
df.iloc[3, 3] = np.nan
df

2. Basic Styling:

Use the style property of the DataFrame:

df.style

3. Color Gradient:

You can add a color gradient to highlight values. This can be particularly useful for heatmaps.

df.style.background_gradient(cmap='viridis')

4. Highlight Max and Min:

Highlight the max value in each column:

df.style.highlight_max(axis=0)

Or the min:

df.style.highlight_min(axis=0)

5. Custom CSS:

You can add custom CSS to a DataFrame using the set_table_styles method:

styles = [
    dict(selector="th", props=[("font-size", "150%"),
                               ("text-align", "center")]),
    dict(selector="td", props=[("font-size", "120%"),
                               ("text-align", "center")])
]

df.style.set_table_styles(styles)

6. Chaining Styles:

Styles can be chained together:

df.style.highlight_max(axis=0).background_gradient(cmap='Blues')

7. Bar Charts:

You can include bars in your DataFrame for quick visualizations:

df.style.bar(subset=['A', 'B'], align='mid', color=['#d65f5f', '#5fba7d'])

Conclusion:

The pandas styling interface provides a wide range of possibilities, from basic color mapping to more advanced CSS. While it doesn't replace full-fledged data visualization tools, it's a handy and quick way to make your DataFrame visually appealing directly within Jupyter Notebooks. Remember, these styles are primarily for visualization in the notebook and won't carry over if you export the DataFrame to other formats like CSV.

  1. Jupyter Notebook custom styling for Pandas DataFrame:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom CSS styles
styles = """
   th {
       background-color: #f2f2f2;
   }
   td {
       font-weight: bold;
       color: blue;
   }
"""

# Display DataFrame with custom styles
display(HTML('<style>{}</style>'.format(styles)))
display(df)

  2. Adding CSS styles to Pandas DataFrame output in Jupyter Notebook:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom CSS styles
styles = {
    'background-color': 'lightblue',
    'color': 'darkred',
    'font-weight': 'bold',
}

# Apply styles to DataFrame
df.style.set_properties(**styles)

  3. HTML and CSS styling in Jupyter Notebook with Pandas:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom HTML and CSS styling
styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles
display(HTML(styles))
display(df)

  4. Jupyter Notebook Markdown cell with Pandas and CSS:

    import pandas as pd
from IPython.display import display, Markdown

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom Markdown cell with CSS styling
markdown_styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles in a Markdown cell
display(Markdown(markdown_styles))
display(df)

  5. Applying custom styles to Pandas DataFrame in Jupyter Notebook:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom styling function
def custom_style(value):
    return 'background-color: lightblue; color: darkred; font-weight: bold;'

# Apply custom styles to DataFrame
df.style.applymap(custom_style)

  6. Jupyter Notebook display options for Pandas with CSS:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Set display options with custom CSS
pd.set_option('display.notebook_repr_html', True)
pd.set_option('display.max_colwidth', 20)
pd.set_option('display.max_columns', 5)

# Custom CSS styles
styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles
display(HTML(styles))
display(df)

  7. HTML and CSS in Jupyter Notebook for visualizing Pandas DataFrames:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom HTML and CSS styling
styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles
display(HTML(styles + df.to_html()))

  8. Embedding CSS styles in Jupyter Notebook cells with Pandas:

    import pandas as pd
from IPython.core.display import HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom CSS styles
styles = """
   th {
       background-color: #f2f2f2;
   }
   td {
       font-weight: bold;
       color: blue;
   }
"""

# Display DataFrame with embedded CSS styles
HTML(df.style.set_table_styles([{'selector': 'thead', 'props': [('background-color', '#f2f2f2')]},
                                {'selector': 'td', 'props': [('font-weight', 'bold'), ('color', 'blue')]}]).render())

  9. Styling options for Pandas DataFrame in Jupyter Notebook:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom styling using the Styler object
styler = df.style \
    .set_properties(**{'background-color': 'lightblue', 'color': 'darkred', 'font-weight': 'bold'}) \
    .set_table_styles([{'selector': 'thead', 'props': [('background-color', '#f2f2f2')]}])

# Display styled DataFrame
styler

  10. Customizing Pandas DataFrame output appearance in Jupyter Notebook:

    import pandas as pd

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom styling using the Styler object
styler = df.style \
    .set_properties(**{'background-color': 'lightblue', 'color': 'darkred', 'font-weight': 'bold'}) \
    .set_table_styles([{'selector': 'thead', 'props': [('background-color', '#f2f2f2')]}])

# Display styled DataFrame
styler

  11. Combining HTML, CSS, and Pandas for enhanced visualization in Jupyter:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom HTML and CSS styling
styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles
display(HTML(styles + df.to_html()))

  12. Using Markdown cells for CSS styling in Jupyter Notebook with Pandas:

    import pandas as pd
from IPython.display import Markdown

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom Markdown cell with CSS styling
markdown_styles = """
   <style>
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Display DataFrame with custom styles in a Markdown cell
display(Markdown(markdown_styles))
display(df)

  13. Advanced Pandas DataFrame styling in Jupyter Notebook:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom advanced styling using the Styler object
styler = df.style \
    .bar(subset=['A'], color='lightblue') \
    .bar(subset=['B'], color='lightgreen') \
    .set_properties(**{'background-color': 'lightyellow', 'color': 'darkred', 'font-weight': 'bold'}) \
    .set_table_styles([{'selector': 'thead', 'props': [('background-color', '#f2f2f2')]}])

# Display styled DataFrame
styler

  14. Visualizing Pandas DataFrames with custom CSS styles in Jupyter:

    import pandas as pd
from IPython.display import display, HTML

# Sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Custom CSS styles for visualization
styles = """
   <style>
       .highlight_max {
           background-color: lightgreen !important;
           color: darkred !important;
       }
       th {
           background-color: #f2f2f2;
       }
       td {
           font-weight: bold;
           color: blue;
       }
   </style>
"""

# Highlight maximum values and display DataFrame with custom styles
display(HTML(styles))
display(df.style.highlight_max(axis=0))
Copyright © devanddep.com | All Rights Reserved